Start with the decision
A useful AI workflow begins with a precise question: what should this system help someone decide or do? Drafting a response, extracting a field, and authorising a transaction are different tasks. They deserve different permissions, checks, and recovery paths. Map the proposed action before choosing a model or interface.
Make review a real part of the workflow
A review button alone does not make oversight useful. The reviewer needs the original request, the relevant source material, the proposed action, and a clear account of what remains uncertain. Design the handoff so a person can correct, reject, or escalate a suggestion without reconstructing the whole process.
Evaluate the difficult cases
Build a small set of representative examples, including incomplete information, conflicting records, and requests the system should decline. Define what a useful response looks like before testing. Record failure modes separately: an incorrect fact, an unsupported recommendation, and an unauthorised action have different consequences.
Keep a path back
Decide which actions can be undone, which require approval, and how the team can pause the workflow. Use appropriate access boundaries and retain only the information needed to understand and operate the system. The engineering question is not simply whether an AI can complete a task, but whether people can understand and manage the result.
A starting point for discussion
This is an engineering perspective, not a report of experimental findings or a claim of certification. NIST’s voluntary AI Risk Management Framework provides broader guidance on incorporating trustworthiness into AI design, development, use, and evaluation. The right controls still depend on the application and its consequences.